Background Diagnosis of hip joint plays an important role in early screening of hip diseases such as coxarthritis, heterotopic ossification, osteonecrosis of the femoral head, etc. Early detection of hip dysplasia on X-ray films may probably conduce to early treatment of patients, which can help to cure patients or relieve their pain as much as possible. There has been no method or tool for automatic diagnosis of hip dysplasia till now. Results A semi-automatic method for diagnosis of hip dysplasia is proposed. Considering the complexity of medical imaging, the contour of acetabulum, femoral head, and the upper side of thigh-bone are manually marked. Feature points are extracted according to marked contours. Traditional knowledge-driven diagnostic criteria is abandoned. Instead, a data-driven diagnostic model for hip dysplasia is presented. Angles including CE, sharp, and Tonnis angle which are commonly measured in clinical diagnosis, are automatically obtained. Samples, each of which consists of these three angle values, are used for clustering according to their densities in a descending order. A three-dimensional normal distribution derived from the cluster is built and regarded as the parametric model for diagnosis of hip dysplasia. Experiments on 143 X-ray films including 286 samples (i.e., 143 left and 143 right hip joints) demonstrate the effectiveness of our method. According to the method, a computer-aided diagnosis tool is developed for the convenience of clinicians, which can be downloaded at http//www.bio-nefu.com/HIPindex/. The data used to support the findings of this study are available from the corresponding authors upon request. Conclusions This data-driven method provides a more objective measurement of the angles. Besides, it provides a new criterion for diagnosis of hip dysplasia other than doctors' experience deriving from knowledge-driven clinical manual, which actually corresponds to very different way for clinical diagnosis of hip dysplasia.Microbiome composition profiles generated from 16S rRNA sequencing have been extensively studied for their usefulness in phenotype trait prediction, including for complex diseases such as diabetes and obesity. These microbiome compositions have typically been quantified in the form of Operational Taxonomic Unit (OTU) count matrices. However, alternate approaches such as Amplicon Sequence Variants (ASV) have been used, as well as the direct use of k-mer sequence counts. https://www.selleckchem.com/products/olprinone.html The overall effect of these different types of predictors when used in concert with various machine learning methods has been difficult to assess, due to varied combinations described in the literature. Here we provide an in-depth investigation of more than 1,000 combinations of these three clustering/counting methods, in combination with varied choices for normalization and filtering, grouping at various taxonomic levels, and the use of more than ten commonly used machine learning methods for phenotype prediction. The use of short k-mers, which have computational advantages and conceptual simplicity, is shown to be effective as a source for microbiome-based prediction. Among machine-learning approaches, tree-based methods show consistent, though modest, advantages in prediction accuracy. We describe the various advantages and disadvantages of combinations in analysis approaches, and provide general observations to serve as a useful guide for future trait-prediction explorations using microbiome data.Transfer tRNAs (tRNAs) are small non-coding RNAs that are highly conserved in all kingdoms of life. Originally discovered as the molecules that deliver amino acids to the growing polypeptide chain during protein synthesis, tRNAs have been believed for a long time to play exclusive role in translation. However, recent studies have identified key roles for tRNAs and tRNA-derived small RNAs in multiple other processes, including regulation of transcription and translation, posttranslational modifications, stress response, and disease. These emerging roles suggest that tRNAs may be central players in the complex machinery of biological regulatory pathways. Here we overview these non-canonical roles of tRNA in normal physiology and disease, focusing largely on eukaryotic and mammalian systems.Cancer is a disease that affects a large number of people all over the world. For treating cancer, nano-drug delivery system has been introduced recently with objective of increasing therapeutic efficiency of chemotherapeutic drug. The main characteristics of this system are the encapsulation of the insoluble chemotherapeutic cargo, increasing the period of circulation in the body, as well as the delivery of the drug at that specific site. Currently, the nano-drug delivery system based on the stimuli response is becoming more popular because of the extra features for controlling the drug release based on the internal atmosphere of cancer. This review provides a summary of different types of internal (pH, redox, enzyme, ROS, hypoxia) stimuli-responsive nanoparticle drug delivery systems as well as perspective for upcoming times.The emergence and global impact of COVID-19 has focused the scientific and medical community on the pivotal influential role of respiratory viruses as causes of severe pneumonia, on the understanding of the underlying pathomechanisms, and on potential treatment for COVID-19. The latter concentrates on four different strategies (i) antiviral treatments to limit the entry of the virus into the cell and its propagation, (ii) anti-inflammatory treatment to reduce the impact of COVID-19 associated inflammation and cytokine storm, (iii) treatment using cardiovascular medication to reduce COVID-19 associated thrombosis and vascular damage, and (iv) treatment to reduce the COVID-19 associated lung injury. Ideally, effective COVID-19 treatment should target as many of these mechanisms as possible arguing for the search of common denominators as potential drug targets. Leukotrienes and their receptors qualify as such targets they are lipid mediators of inflammation and tissue damage and well-established targets in respiratory diseases like asthma.